We conduct a detailed investigation of correlations between real-time expressions of individuals made across the United States and a wide range of emotional, geographic, demographic, and health characteristics. We do so by combining (1) a massive, geo-tagged data set comprising over 80 million words generated over the course of several recent years on the social network service Twitter and (2) annually-surveyed characteristics of all 50 states and close to 400 urban populations. Among many results, we generate taxonomies of states and cities based on their similarities in word use; estimate the happiness levels of states and cities; correlate highly-resolved demographic characteristics with happiness levels; and connect word choice and message length with urban characteristics such as education levels and obesity rates. Our results show how social media may potentially be used to estimate real-time levels and changes in population-level measures such as obesity rates.

The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place

The World Health Organization (WHO) and news reports are describing the deployment of a new experimental vaccine for Ebola in the Democratic Republic of the Congo (DRC). Originally 4,000 doses were sent to the country, and while the number is growing to 8,000 or more, there are still not enough to widely inoculate the city of Mbandaka with a population of over a million. Reports describe how the vaccine will be used in a "ring vaccination" technique. In ring vaccination, those who are most likely to be infected receive the vaccine. Currently this is being done by inoculating the known contacts of the sick and the contacts of the contacts, as well as healthcare workers. Prior experiments suggest that the vaccine can prevent the disease in those individuals.

Yaneer Bar-Yam, Will the new ring vaccination stop the spread of Ebola?, New England Complex Systems Institute (May 23, 2018).

We investigate the implications of the persistence of traditional patterns of state organization by examining the relationship between property rights and the economy for monarchies and republics. We argue that, relative to republics, monarchies protect property rights to a greater extent by reducing the negative effects of internal conflict, executive tenure, and executive discretion. In turn, a better protection of property rights results in greater standards of living. Using panel data on 137 countries between 1900 and 2010, we formulate and test a model with endogenous variables. We find strong evidence that monarchies contribute to a greater protection of property rights and higher standards of living through each of the three theoretical mechanisms compared to all republics. We also find that democratic-constitutional monarchies perform better than non-democratic and absolute monarchies when it comes to offsetting the negative effects of the tenure and discretion of the executive branch. We discuss the implications of the persistence of traditional patterns of political authority and rule for political sociology and economic sociology.

Almost as soon as antibiotics were discovered to be valuable in medicine, resistance emerged among bacteria. Whenever mutating or recombining organisms are faced with extirpation, those individuals with variations that avert death will survive and reproduce to take over the population. This can happen rapidly among organisms that reproduce fast and outpace our efforts to combat them. Thus, our use of chemical entities to rid ourselves of clinical, domestic, and agricultural pathogens and pests has selected for resistance.

Today, we find ourselves at the nexus of an alarming acceleration of resistance to antibiotics, insecticides, and herbicides. Through chemical misuse, resistance also brings widespread collateral damage to natural, social, and economic systems. Resistance to antifungal agents poses a particular challenge because a limited suite of chemicals is used in both agricultural and clinical settings.

The reproductive potential of pathogens is linked inextricably to the host social behavior required for transmission. We propose that future work should consider contact periodicity in models of disease dynamics, and suggest the possibility that disease control strategies may be designed to optimize against the effects of synchronization.

Many have heard of Alan Turing, the mathematician and logician who invented modern computing in 1935. They know Turing, the cryptologist who cracked the Nazi Enigma code, helped win World War II. And they remember Turing as a martyr for gay rights who, after being prosecuted and sentenced to chemical castration, committed suicide by eating an apple laced with cyanide in 1954.

But few have heard of Turing, the naturalist who explained patterns in nature with math. Nearly half a century after publishing his final paper in 1952, chemists and biological mathematicians came to appreciate the power of his late work to explain problems they were solving, like how zebrafish get their stripes or cheetahs get spots. And even now, scientists are finding new insights from Turing’s legacy.

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students.

Technological advances have led to a strong increase in the number of data collection efforts aimed at measuring co-presence of individuals at different spatial resolutions. It is however unclear how much co-presence data can inform us on actual face-to-face contacts, of particular interest to study the structure of a population in social groups or for use in data-driven models of information or epidemic spreading processes. Here, we address this issue by leveraging data sets containing high resolution face-to-face contacts as well as a coarser spatial localisation of individuals, both temporally resolved, in various contexts. The co-presence and the face-to-face contact temporal networks share a number of structural and statistical features, but the former is (by definition) much denser than the latter. We thus consider several down-sampling methods that generate surrogate contact networks from the co-presence signal and compare them with the real face-to-face data. We show that these surrogate networks reproduce some features of the real data but are only partially able to identify the most central nodes of the face-to-face network. We then address the issue of using such down-sampled co-presence data in data-driven simulations of epidemic processes, and in identifying efficient containment strategies. We show that the performance of the various sampling methods strongly varies depending on context. We discuss the consequences of our results with respect to data collection strategies and methodologies.

Multifractal analysis offers a number of advantages to measure spatial economic segregation and inequality, as it is free of categories and boundaries definition problems and is insensitive to some shape-preserving changes in the variable distribution. We use two datasets describing Kyoto land prices in 1912 and 2012 and derive city models from this data to show that multifractal analysis is suitable to describe the heterogeneity of land prices. We found in particular a sharp decrease in multifractality, characteristic of homogenisation, between older Kyoto and present Kyoto, and similarities both between present Kyoto and present London, and between Kyoto and Manhattan as they were a century ago. In addition, we enlighten the preponderance of spatial distribution over variable distribution in shaping the multifractal spectrum. The results were tested against the classical segregation and inequality indicators, and found to offer an improvement over those.

Understanding how a scientist develops new scientific collaborations or how their papers receive new citations is a major challenge in scientometrics. The approach being proposed simultaneously examines the growth processes of the co-authorship and citation networks by analyzing the evolutions of the rich get richer and the fit get richer phenomena. In particular, the preferential attachment function and author fitnesses, which govern the two phenomena, are estimated non-parametrically in each network. The approach is applied to the co-authorship and citation networks of the flagship journal of the strategic management scientific community, namely the Strategic Management Journal. The results suggest that the abovementioned phenomena have been consistently governing both temporal networks. The average of the attachment exponents in the co-authorship network is 0.30 while it is 0.29 in the citation network. This suggests that the rich get richer phenomenon has been weak in both networks. The right tails of the distributions of author fitness in both networks are heavy, which imply that the intrinsic scientific quality of each author has been playing a crucial role in getting new citations and new co-authorships. Since the total competitiveness in each temporal network is founded to be rising with time, it is getting harder to receive a new citation or to develop a new collaboration. Analyzing the average competency, it was found that on average, while the veterans tend to be more competent at developing new collaborations, the newcomers are likely better at acquiring new citations. Furthermore, the author fitness in both networks has been consistent with the history of the strategic management scientific community. This suggests that coupling node fitnesses throughout different networks might be a promising new direction in analyzing simultaneously multiple networks.

The evolutions of the rich get richer and the fit get richer phenomena in scholarly networks: the case of the strategic management journal

The advancement of network science over the past 20 years has created the expectation that we will soon be able to systematically control the behavior of complex network systems and in turn address numerous outstanding scientific problems, from cell reprogramming and drug target identification to cascade control and self-healing infrastructure development [4]. This expectation is not without reason, given that control technologies have been part of human development for over 2,000 years [1].

While significant progress has been made, our current ability to control is still limited in many systems. This is not so much from lack of available technologies to actuate specific network elements as from challenges imposed by unique characteristics of large real networks to designing system-level control actions [4]. These limiting characteristics include the combination of high dimensionality, nonlinearity, and constraints on the interventions, which set networks apart from other systems to which control has been traditionally applied [1]. Recent progress on developing control techniques scalable to large networks has been driven by the design of new approaches.

The ‘free energy principle’ (FEP) has been suggested to provide a unified theory of the brain, integrating data and theory relating to action, perception, and learning. The theory and implementation of the FEP combines insights from Helmholtzian ‘perception as inference’, machine learning theory, and statistical thermodynamics. Here, we provide a detailed mathematical evaluation of a suggested biologically plausible implementation of the FEP that has been widely used to develop the theory. Our objectives are (i) to describe within a single article the mathematical structure of this implementation of the FEP; (ii) provide a simple but complete agent-based model utilising the FEP and (iii) to disclose the assumption structure of this implementation of the FEP to help elucidate its significance for the brain sciences.

What is the optimal level of chaos in a computational system? If a system is too chaotic, it cannot reliably store information. If it is too ordered, it cannot transmit information. A variety of computational systems exhibit dynamics at the “edge of chaos”, the transition between the ordered and chaotic regimes. In this work, we examine the evolved neural networks of Polyworld, an artificial life model consisting of a simulated ecology populated with biologically inspired agents. As these agents adapt to their environment, their initially simple neural networks become increasingly capable of exhibiting rich dynamics. Dynamical systems analysis reveals that natural selection drives these networks toward the edge of chaos until the agent population is able to sustain itself. After this point, the evolutionary trend stabilizes, with neural dynamics remaining on average significantly far from the transition to chaos.

Evolution of Neural Dynamics in an Ecological ModelSteven Williams and Larry Yaeger

Unlike Derek Zoolander, ants don't have any difficulty turning left. New research from the University of Bristol has now found rock ants often have one eye slightly better than the other, which could help explain why most of them prefer to turn left, given the choice.

Animals as diverse as ants and humans are faced with the tasks of collecting, transporting or herding objects. Sheepdogs do this daily when they collect, herd, and maneuver flocks of sheep. Here, we adapt a shepherding algorithm inspired by sheepdogs to collect and transport objects using a robot. Our approach produces an effective robot collection process that autonomously adapts to changing environmental conditions and is robust to noise from various sources. We suggest that this biomimetic process could be implemented into suitable robots to perform collection and transport tasks that might include – for example – cleaning up objects in the environment, keeping animals away from sensitive areas or collecting and herding animals to a specific location. Furthermore, the feedback controlled interactions between the robot and objects which we study can be used to interrogate and understand the local and global interactions of real animal groups, thus offering a novel methodology of value to researchers studying collective animal behavior.

Dueling neural networks. Artificial embryos. AI in the cloud. Welcome to our annual list of the 10 technology advances we think will shape the way we work and live now and for years to come.

Every year since 2001 the people at Technology Review have picked what they call the 10 Breakthrough Technologies. People often ask, what exactly is meant by “breakthrough”? It’s a reasonable question—some of the picks haven’t yet reached widespread use, while others may be on the cusp of becoming commercially available. What Technology Review is really looking for is a technology, or perhaps even a collection of technologies, that will have a profound effect on our lives.

For 2018, a new technique in artificial intelligence called GANs is giving machines imagination; artificial embryos, despite some thorny ethical constraints, are redefining how life can be created and are opening a research window into the early moments of a human life; and a pilot plant in the heart of Texas’s petrochemical industry is attempting to create completely clean power from natural gas—probably a major energy source for the foreseeable future.

In most fields of science, medicine, and technology research, men comprise more than half of the workforce, particularly at senior levels. Most previous work has concluded that the gender gap is smaller today than it was in the past, giving the impression that there will soon be equal numbers of men and women researchers and that current initiatives to recruit and retain more women are working adequately. Here, we used computational methods to determine the numbers of men and women authors listed on >10 million academic papers published since 2002, allowing us to precisely estimate the gender gap among researchers, as well as its rate of change, for most disciplines of science and medicine. We conclude that many research specialties (e.g., surgery, computer science, physics, and maths) will not reach gender parity this century, given present-day rates of increase in the number of women authors. Additionally, the gender gap varies greatly across countries, with Japan, Germany, and Switzerland having strikingly few women authors. Women were less often commissioned to write ‘invited’ papers, consistent with gender bias by journal editors, and were less often found in authorship positions usually associated with seniority (i.e., the last-listed or sole author). Our results support a need for further reforms to close the gender gap.

Perceptual generalization and discrimination are fundamental cognitive abilities. For example, if a bird eats a poisonous butterfly, it will learn to avoid preying on that species again by generalizing its past experience to new perceptual stimuli. In cognitive science, the “universal law of generalization” seeks to explain this ability and states that generalization between stimuli will follow an exponential function of their distance in “psychological space.” Here, I challenge existing theoretical explanations for the universal law and offer an alternative account based on the principle of efficient coding. I show that the universal law emerges inevitably from any information processing system (whether biological or artificial) that minimizes the cost of perceptual error subject to constraints on the ability to process or transmit information.

Efficient coding explains the universal law of generalization in human perceptionChris R. Sims

Activity network analysis is a widely used tool for managing project risk. Traditionally, this type of analysis is used to evaluate task criticality by assuming linear cause‐and‐effect phenomena, where the size of a local failure (e.g. task delay) dictates its possible global impact (e.g. project delay). Motivated by the question of whether activity networks are subject to non‐linear cause‐and‐effect phenomena, a computational framework is developed and applied to real‐world project data to evaluate project systemic risk. Specifically, project systemic risk is viewed as the result of a cascading process which unravels across an activity network, where the failure of a single task can consequently affect its immediate, downstream task(s). As a result, we demonstrate that local failures are capable of triggering failure cascades of intermittent sizes. In turn, a modest local disruption can fuel exceedingly large, systemic failures. In addition, the probability for this to happen is much higher than anticipated. A systematic examination of why this is the case is subsequently performed, with results attributing the emergence of large‐scale failures to topological and temporal features of activity networks. Finally, local mitigation is assessed in terms of containing these failures cascades – results illustrate that this form of mitigation is both ineffective and insufficient. Given the ubiquity of our findings, our work has the potential of deepening our current theoretical understanding on the causal mechanisms responsible for large‐scale project failures.

The domino effect: an empirical exposition of systemic risk across project networks

Urban economists have put forward the idea that cities that are culturally interesting tend to attract “the creative class” and, as a result, end up being economically successful. Yet it is still unclear how economic and cultural dynamics mutually influence each other. By contrast, that has been extensively studied in the case of individuals. Over decades, the French sociologist Pierre Bourdieu showed that people's success and their positions in society mainly depend on how much they can spend (their economic capital) and what their interests are (their cultural capital). For the first time, we adapt Bourdieu's framework to the city context. We operationalize a neighborhood's cultural capital in terms of the cultural interests that pictures geo-referenced in the neighborhood tend to express. This is made possible by the mining of what users of the photo-sharing site of Flickr have posted in the cities of London and New York over 5 years. In so doing, we are able to show that economic capital alone does not explain urban development. The combination of cultural capital and economic capital, instead, is more indicative of neighborhood growth in terms of house prices and improvements of socio-economic conditions. Culture pays, but only up to a point as it comes with one of the most vexing urban challenges: that of gentrification.

The complex dynamics of gene expression in living cells can be well-approximated using Boolean networks. The average sensitivity is a natural measure of stability in these systems: values below one indicate typically stable dynamics associated with an ordered phase, whereas values above one indicate chaotic dynamics. This yields a theoretically motivated adaptive advantage to being near the critical value of one, at the boundary between order and chaos. Here, we measure average sensitivity for 66 publicly available Boolean network models describing the function of gene regulatory circuits across diverse living processes. We find the average sensitivity values for these networks are clustered around unity, indicating they are near critical. In many types of random networks, mean connectivity <K> and the average activity bias of the logic functions <p> have been found to be the most important network properties in determining average sensitivity, and by extension a network's criticality. Surprisingly, many of these gene regulatory networks achieve the near-critical state with <K> and <p> far from that predicted for critical systems: randomized networks sharing the local causal structure and local logic of biological networks better reproduce their critical behavior than controlling for macroscale properties such as <K> and <p> alone. This suggests the local properties of genes interacting within regulatory networks are selected to collectively be near-critical, and this non-local property of gene regulatory network dynamics cannot be predicted using the density of interactions alone.

This paper investigates a consensus-based auction algorithm in the context of decentralized traffic control. In particular, we study the automation of a road intersection, where a set of vehicles is required to cross without collisions. The crossing order will be negotiated in a decentralized fashion. An on-board model predictive controller (MPC) will compute an optimal trajectory which avoids collisions with higher priority vehicles, thus retaining convex safety constraints. Simulations are then performed in a time-variant traffic environment.

Sewall Wright's fitness landscape introduced the concept of evolutionary spaces in 1932. George Gaylord Simpson modified this to an adaptive, phenotypic landscape in 1944 and since then evolutionary spaces have played an important role in evolutionary theory through fitness and adaptive landscapes, phenotypic and functional trait spaces, morphospaces and related concepts. Although the topology of such spaces is highly variable, from locally Euclidean to pre-topological, evolutionary change has often been interpreted as a search through a pre-existing space of possibilities, with novelty arising by accessing previously inaccessible or difficult to reach regions of a space. Here I discuss the nature of evolutionary novelty and innovation within the context of evolutionary spaces, and argue that the primacy of search as a conceptual metaphor ignores the generation of new spaces as well as other changes that have played important evolutionary roles.

The topology of evolutionary novelty and innovation in macroevolutionDouglas H. Erwin

The ubiquity of scale-free mobility in nature, as observed in systems ranging from microorganisms to fishing boats, has stimulated a number of foraging theories and individual-based random search models. Here, we unveil an essential yet unexplored property of multiple-scale motion, which relates to the stability of entire populations. We use Lotka–Volterra models to predict that foragers diffusing normally tend to go extinct in fragile fragmented ecosystems, whereas their populations become resilient to degraded conditions and have maximized abundances when individuals perform scale-free Lévy flights. Our analytical and simulated results shift the scope of multiple-scale foraging from the individual level to the scales of collective phenomena that are of primary interest in conservation biology.

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